{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "876afb54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'confirm': 55,\n",
       " 'heal': 48,\n",
       " 'dead': 0,\n",
       " 'nowConfirm': 7,\n",
       " 'suspect': 0,\n",
       " 'nowSevere': -1,\n",
       " 'importedCase': 18,\n",
       " 'noInfect': 22,\n",
       " 'localConfirm': 1,\n",
       " 'noInfectH5': 0,\n",
       " 'localConfirmH5': 21}"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "import json\n",
    "import requests\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "def catch_data():\n",
    "    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'\n",
    "    reponse = requests.get(url=url).json()\n",
    "    #返回数据字典\n",
    "    data = json.loads(reponse['data'])\n",
    "    return data\n",
    "data = catch_data()\n",
    "# 获取数据的键值\n",
    "data.keys()\n",
    "# 获取数据的最后更新时间\n",
    "lastUpdateTime = data['lastUpdateTime']\n",
    "# lastUpdateTime\n",
    "chinaTotal = data['chinaTotal']\n",
    "# chinaTotal\n",
    "chinaAdd = data['chinaAdd']\n",
    "# 累计确诊总计\n",
    "# print(chinaTotal)\n",
    "# # 较昨日新增\n",
    "# print(chinaAdd)\n",
    "\n",
    "chinaAdd\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa3f84d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data\n",
    "# data['chinaTotal']\n",
    "# data['areaTree']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bd5c06e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# newadd=chinaAdd\n",
    "# newadd['累计新增确诊']=newadd.pop('confirm')\n",
    "# newadd['较昨日新增确诊']=newadd.pop('nowConfirm')\n",
    "# newadd['较昨日康复']=newadd.pop('heal')\n",
    "# newadd['境外新增']=newadd.pop('importedCase')\n",
    "# newadd['无症状感染者']=newadd.pop('noInfect')\n",
    "# newadd['较昨日新增确诊']=newadd.pop('IocalConfirmH5')\n",
    "# newadd['新增死亡']=newadd.pop('dead')\n",
    "# newadd['本土新增确诊']=newadd.pop('localConfirmH5')\n",
    "# del newadd['suspect']\n",
    "# newadd.pop('nowSevere')\n",
    "# newadd.pop('localConfirm')\n",
    "# newadd.pop('noInfectH5')\n",
    "# newadd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48b32a9a",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pyecharts'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-8a677c630d86>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpyecharts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharts\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mPie\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpyecharts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mopts\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromtimestamp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mstr1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%Y-%m-%d %H:%M:%S\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pyecharts'"
     ]
    }
   ],
   "source": [
    "from pyecharts.charts import Pie\n",
    "import pyecharts.options as opts\n",
    "import time,datetime\n",
    "time=datetime.datetime.fromtimestamp(time.time())\n",
    "str1 = time.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "(\n",
    "    Pie(init_opts=opts.InitOpts(width='720px',height='320px'))\n",
    "    .add(series_name='', data_pair=[list(z) for z in zip(chinaAdd.keys(), chinaAdd.values())])\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=str1+'较昨日新增'),\n",
    "                    legend_opts=opts.LegendOpts(type_='scroll',pos_right='right',orient='vertical'))\n",
    "\n",
    ").render_notebook()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eaacd87b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#获取全国各地的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ed97deb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>today</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>地区待确认</td>\n",
       "      <td>台湾</td>\n",
       "      <td>{'confirm': 0, 'confirmCuts': 0, 'isUpdated': ...</td>\n",
       "      <td>{'nowConfirm': 2006, 'confirm': 16596, 'suspec...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>大连</td>\n",
       "      <td>辽宁</td>\n",
       "      <td>{'confirm': 0, 'confirmCuts': 0, 'isUpdated': ...</td>\n",
       "      <td>{'nowConfirm': 146, 'confirm': 470, 'suspect':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>境外输入</td>\n",
       "      <td>辽宁</td>\n",
       "      <td>{'confirm': 0, 'confirmCuts': 0, 'isUpdated': ...</td>\n",
       "      <td>{'nowConfirm': 5, 'confirm': 146, 'suspect': 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>丹东</td>\n",
       "      <td>辽宁</td>\n",
       "      <td>{'confirm': 0, 'confirmCuts': 0, 'isUpdated': ...</td>\n",
       "      <td>{'nowConfirm': 0, 'confirm': 11, 'suspect': 0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>地区待确认</td>\n",
       "      <td>辽宁</td>\n",
       "      <td>{'confirm': 0, 'confirmCuts': 0, 'isUpdated': ...</td>\n",
       "      <td>{'nowConfirm': 0, 'confirm': 0, 'suspect': 0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    city province                                              today  \\\n",
       "0  地区待确认       台湾  {'confirm': 0, 'confirmCuts': 0, 'isUpdated': ...   \n",
       "1     大连       辽宁  {'confirm': 0, 'confirmCuts': 0, 'isUpdated': ...   \n",
       "2   境外输入       辽宁  {'confirm': 0, 'confirmCuts': 0, 'isUpdated': ...   \n",
       "3     丹东       辽宁  {'confirm': 0, 'confirmCuts': 0, 'isUpdated': ...   \n",
       "4  地区待确认       辽宁  {'confirm': 0, 'confirmCuts': 0, 'isUpdated': ...   \n",
       "\n",
       "                                               total  \n",
       "0  {'nowConfirm': 2006, 'confirm': 16596, 'suspec...  \n",
       "1  {'nowConfirm': 146, 'confirm': 470, 'suspect':...  \n",
       "2  {'nowConfirm': 5, 'confirm': 146, 'suspect': 0...  \n",
       "3  {'nowConfirm': 0, 'confirm': 11, 'suspect': 0,...  \n",
       "4  {'nowConfirm': 0, 'confirm': 0, 'suspect': 0, ...  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据明细，数据结构比较复杂，一步一步打印出来看，先明白数据结构\n",
    "areaTree = data['areaTree']\n",
    "# 国内数据,获取每个省份的数据信息\n",
    "china_data = areaTree[0]['children']\n",
    "# 新建一个列表\n",
    "china_list = []\n",
    "for a in range(len(china_data)):\n",
    "#     省份名称\n",
    "    province = china_data[a]['name']\n",
    "#     获取省份所在的地区\n",
    "    province_list = china_data[a]['children']\n",
    "#     遍历省份所在的地区的有关信息\n",
    "    for b in range(len(province_list)):\n",
    "        city = province_list[b]['name']\n",
    "        total = province_list[b]['total']\n",
    "        today = province_list[b]['today']\n",
    "        china_dict = {}\n",
    "        china_dict['province'] = province\n",
    "        china_dict['city'] = city\n",
    "        china_dict['total'] = total\n",
    "        china_dict['today'] = today\n",
    "        china_list.append(china_dict)\n",
    "\n",
    "china_data = pd.DataFrame(china_list)\n",
    "china_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e162fd2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义数据处理函数\n",
    "def now_confirm(x):\n",
    "    now_confirm=eval(str(x))['nowConfirm']\n",
    "    return now_confirm\n",
    "def confirm(x):\n",
    "    confirm = eval(str(x))['confirm']\n",
    "    return confirm\n",
    "def dead(x):\n",
    "    dead = eval(str(x))['dead']\n",
    "    return dead\n",
    "def heal(x):\n",
    "    heal =  eval(str(x))['heal']\n",
    "    return heal\n",
    "def grade(x):\n",
    "    if 'grade' in list(x.keys()):\n",
    "        if x['nowConfirm']==0:\n",
    "            grade='无风险'\n",
    "        else:\n",
    "            grade=x['grade']\n",
    "    else:\n",
    "        grade='无风险'\n",
    "    return grade\n",
    "    \n",
    "#  函数映射\n",
    "china_data['nowconfirm'] = china_data['total'].map(now_confirm)\n",
    "# china_data['sumconfirm'] = china_data['total'].map(confirm)\n",
    "# china_data['dead'] = china_data['total'].map(dead)\n",
    "# china_data['heal'] = china_data['total'].map(heal)\n",
    "china_data['grade'] = china_data['total'].map(grade)\n",
    "\n",
    "# china_data01 = china_data[[\"province\",\"city\",\"nowconfirm\",\"sumconfirm\",\"dead\",\"heal\"]]\n",
    "china_data02=china_data[['province','city','nowconfirm','grade']]\n",
    "china_data02.to_csv('china_province_city_grade_11_30.csv',encoding='utf_8_sig')\n",
    "\n",
    "# china_data01.to_csv('china_province_city_11_30.csv',encoding='utf_8_sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "758f94cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提取我们需要的数据\n",
    "# area_data = china_data.groupby(\"province\")[\"confirm\"].sum().reset_index()\n",
    "# area_data.columns = [\"province\",\"confirm\"]\n",
    "# print(area_data )\n",
    "# for i in china_data.total:\n",
    "#     if 'grade' in list(i.keys()):\n",
    "#         print(i['grade'])\n",
    "#     else:\n",
    "#         print('无风险')\n",
    "#     print(list(i.keys()))\n",
    "#     for j in i:\n",
    "#         print(type(j))\n",
    "#         if grade in j:\n",
    "#             print(j['grade'])\n",
    "#         else:\n",
    "#             print(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e9e91ca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import json\n",
    "import requests\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "def catch_data():\n",
    "    url = 'https://api.inews.qq.com/newsqa/v1/automation/modules/list?modules=WomAboard'\n",
    "    reponse = requests.get(url=url)\n",
    "    #返回数据字典\n",
    "    data = reponse.text\n",
    "    return data\n",
    "overseas_data = eval(catch_data())['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3a8f1608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>country</th>\n",
       "      <th>addconfirm</th>\n",
       "      <th>nowconfirm</th>\n",
       "      <th>sumconfirm</th>\n",
       "      <th>dead</th>\n",
       "      <th>heal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北美洲</td>\n",
       "      <td>美国</td>\n",
       "      <td>52274</td>\n",
       "      <td>9462006</td>\n",
       "      <td>49215779</td>\n",
       "      <td>800791</td>\n",
       "      <td>38952982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>亚洲</td>\n",
       "      <td>印度</td>\n",
       "      <td>8309</td>\n",
       "      <td>113764</td>\n",
       "      <td>34580832</td>\n",
       "      <td>468790</td>\n",
       "      <td>33998278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>南美洲</td>\n",
       "      <td>巴西</td>\n",
       "      <td>4043</td>\n",
       "      <td>173278</td>\n",
       "      <td>22080906</td>\n",
       "      <td>614314</td>\n",
       "      <td>21293314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>欧洲</td>\n",
       "      <td>英国</td>\n",
       "      <td>37534</td>\n",
       "      <td>1016647</td>\n",
       "      <td>10146476</td>\n",
       "      <td>144775</td>\n",
       "      <td>8985054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>欧洲</td>\n",
       "      <td>俄罗斯</td>\n",
       "      <td>33548</td>\n",
       "      <td>1029507</td>\n",
       "      <td>9570373</td>\n",
       "      <td>272755</td>\n",
       "      <td>8268111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  continent country  addconfirm  nowconfirm  sumconfirm    dead      heal\n",
       "0       北美洲      美国       52274     9462006    49215779  800791  38952982\n",
       "1        亚洲      印度        8309      113764    34580832  468790  33998278\n",
       "2       南美洲      巴西        4043      173278    22080906  614314  21293314\n",
       "3        欧洲      英国       37534     1016647    10146476  144775   8985054\n",
       "4        欧洲     俄罗斯       33548     1029507     9570373  272755   8268111"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "over_data=overseas_data['WomAboard']\n",
    "# 获取每个大洲\n",
    "country_list=[]\n",
    "for i in range(len(over_data)):\n",
    "#     获取每个大洲\n",
    "    continent=over_data[i]['continent']\n",
    "#     获取每个大洲所在的国家\n",
    "    country=over_data[i]['name']\n",
    "    addconfirm=over_data[i]['confirmAdd']\n",
    "    nowconfirm=over_data[i]['nowConfirm']\n",
    "    sumconfirm=over_data[i]['confirm']\n",
    "    dead=over_data[i]['dead']\n",
    "    heal=over_data[i]['heal']\n",
    "    all_country={}\n",
    "    all_country['continent']=continent\n",
    "    all_country['country']=country\n",
    "    all_country['addconfirm']=addconfirm\n",
    "    all_country['nowconfirm']=nowconfirm\n",
    "    all_country['sumconfirm']=sumconfirm\n",
    "    all_country['dead']=dead\n",
    "    all_country['heal']=heal\n",
    "    country_list.append(all_country)\n",
    "country_data=pd.DataFrame(country_list)\n",
    "country_data01=country_data[['continent','country','addconfirm','nowconfirm','sumconfirm','dead','heal']]\n",
    "country_data01.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2c50c23a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# country_data01.to_csv('world_data_11_30.csv',encoding='utf_8_sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6b0376f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 爬取新型冠状疫苗\n",
    "import time\n",
    "import json\n",
    "import requests\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "def catch_data():\n",
    "    url = 'https://api.inews.qq.com/newsqa/v1/automation/modules/list?modules=VaccineSituationData'\n",
    "    reponse = requests.get(url=url)\n",
    "    #返回数据字典\n",
    "    data = reponse.text\n",
    "    return data\n",
    "vaccine_data=catch_data()\n",
    "# vaccine_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4dbb9cef",
   "metadata": {},
   "outputs": [],
   "source": [
    "data01=eval(vaccine_data)['data']['VaccineSituationData']\n",
    "vaccine_country_data=[]\n",
    "for i in range(len(data01)):\n",
    "    country=data01[i]['country']\n",
    "    date=data01[i]['date']\n",
    "    vaccinations=data01[i]['vaccinations']\n",
    "    total_numbers=data01[i]['total_vaccinations']\n",
    "    total_100_num=data01[i]['total_vaccinations_per_hundred']\n",
    "    country_list={}\n",
    "    country_list['country']=country\n",
    "    country_list['date']=date\n",
    "    country_list['vaccinations_type']=vaccinations\n",
    "    country_list['total_vacc_numbers']=total_numbers\n",
    "    country_list['vacc_100_nums']=total_100_num\n",
    "    vaccine_country_data.append(country_list)\n",
    "country_vaccine_data=pd.DataFrame(vaccine_country_data)\n",
    "country_vaccine_data.to_csv('country_vaccine_data_11_30.csv',encoding='utf_8_sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81e7c8cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d138f420",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66487523",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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